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Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism
by
Linhardt, Lorenz
, Engels, Christoph
, Schubert, Marcel
in
Age discrimination
/ Algorithms
/ Analysis
/ Bias
/ Bias (Law)
/ Courts
/ Crime
/ Criminal behavior, Prediction of
/ Defendants
/ Demographic aspects
/ Judges & magistrates
/ Judicial process
/ Judiciary
/ Laws, regulations and rules
/ Normativity (Ethics)
/ Predictions
/ Prevention
/ Race
/ Race discrimination
/ Racial bias
/ Recidivism
/ Risk
/ Risk assessment
/ States
/ Technology application
/ Victims
2025
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Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism
by
Linhardt, Lorenz
, Engels, Christoph
, Schubert, Marcel
in
Age discrimination
/ Algorithms
/ Analysis
/ Bias
/ Bias (Law)
/ Courts
/ Crime
/ Criminal behavior, Prediction of
/ Defendants
/ Demographic aspects
/ Judges & magistrates
/ Judicial process
/ Judiciary
/ Laws, regulations and rules
/ Normativity (Ethics)
/ Predictions
/ Prevention
/ Race
/ Race discrimination
/ Racial bias
/ Recidivism
/ Risk
/ Risk assessment
/ States
/ Technology application
/ Victims
2025
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Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism
by
Linhardt, Lorenz
, Engels, Christoph
, Schubert, Marcel
in
Age discrimination
/ Algorithms
/ Analysis
/ Bias
/ Bias (Law)
/ Courts
/ Crime
/ Criminal behavior, Prediction of
/ Defendants
/ Demographic aspects
/ Judges & magistrates
/ Judicial process
/ Judiciary
/ Laws, regulations and rules
/ Normativity (Ethics)
/ Predictions
/ Prevention
/ Race
/ Race discrimination
/ Racial bias
/ Recidivism
/ Risk
/ Risk assessment
/ States
/ Technology application
/ Victims
2025
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Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism
Journal Article
Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism
2025
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Overview
Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction increases overall accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice.
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